The computational fluid dynamic (CFD) based energy improvement of the parametric blade model for a Francis turbine runner is presented. The evaluation of the energy improved uses the results of CFD based optimization of a hydraulic Francis turbine runner. The parametric runner model used by the CFD based optimization process was obtained by applying a parametric blade modeller for turbomachinery based on a geometric reference model. This parametric runner model and the optimization process were computed by using a three dimensional Navier-Stoke commercial turbomachinery oriented CFD code. The flow within hydraulic turbines has a thin boundary layer and noticeable pressure gradients. Hence, the CFD computations were carried out using the Sparlat-Allmaras turbulence model. The aim of the optimization process was improve the performance of the machine. This process was computed by a CFD code integrated environment which combines genetic algorithms and a trained artificial neural network. After optimization cycle convergence, an increment not only in efficiency but also in power was obtained. The energy that is transferred to the runner blade and transformed in torque and power was obtained by using CFD results. From pressure distribution along the normalized arc length of the runner blade for three operating conditions (100%, 85% and, 75% of load) the energy distribution was computed not only for the reference runner but also for the optimized parametric model of the turbine runner. Finally, the averaged energy saved for the same operating conditions was evaluated. Results have shown that application of CFD based optimization can modify and improve runners design so as to increase the efficiency and power of installed hydraulic power stations.

This content is only available via PDF.
You do not currently have access to this content.